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Evaluation of machine learning algorithms for image quality assessment

Auteur(s) : G. Takam Tchendjou, R. Alhakim, E. Simeu, F. Lebowsky

Doc. Source: International Symposium on On-Line Test and Robust System Design (IOLTS 2016)

Publisher : IEEE

Pages : 193-194

Doi : 10.1109/IOLTS.2016.7604697

In this article, we apply different machine learning (ML) techniques for building objective models, that permit to automatically assess the image quality in agreement with human visual perception. The six ML methods proposed are discriminant analysis, k-nearest neighbors, artificial neural network, non-linear regression, decision tree and fuzzy logic. Both the stability and the robustness of designed models are evaluated by using Monte-Carlo cross-validation approach (MCCV). The simulation results demonstrate that fuzzy logic model provides the best prediction accuracy.